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NADM: Noise-Aware Diffusion Model for Landscape Painting Video Generation
IEEE Transactions on Cybernetics
|June 24, 2025
Summary
This study introduces a new dataset and a noise-aware diffusion model (NADM) for generating dynamic landscape painting videos. The approach enhances artistic video generation by capturing aesthetic dynamism and smooth transitions.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Digital Art
Background:
- Traditional landscape paintings are static, limiting the viewer's imagination of dynamic scenes.
- Emerging text-to-video (T2V) models generate natural videos but struggle with artistic aesthetics and specific datasets.
- Generating high-quality, dynamic landscape painting videos presents unique challenges due to style intricacy and data scarcity.
Purpose of the Study:
- To develop a novel text-to-video (T2V) dataset specifically for landscape painting videos.
- To propose a new T2V model, the noise-aware diffusion model (NADM), for generating dynamic and aesthetically pleasing landscape painting videos.
- To address the limitations of current T2V methods in capturing the dynamic aesthetic of artistic videos.
Main Methods:
- Introduced the landscape painting videos-high definition (LPV-HD) dataset.
- Developed the noise-aware diffusion model (NADM) based on Stable Diffusion.
- Implemented a motion module with a dual attention mechanism for dynamic image transformations.
- Utilized a noise adapter with unsupervised contrastive learning for latent space beauty enhancement.
- Employed optical flow for frame interpolation to improve video smoothness.
Main Results:
- The proposed NADM successfully generates dynamic landscape painting videos that retain the essence of the original artworks.
- The dual attention mechanism effectively captures dynamic transformations in landscape imagery.
- The noise adapter and frame interpolation contribute to overall aesthetic quality and video smoothness.
- The LPV-HD dataset provides a valuable resource for future research in artistic video generation.
Conclusions:
- The NADM and LPV-HD dataset represent a significant advancement in generating dynamic artistic videos.
- The method successfully balances the preservation of artistic essence with the creation of dynamic visual experiences.
- This work opens new possibilities for digital art creation and the animation of traditional artworks.
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